An estimating algorithm for English levels of student and difficulties of test question

In view of the problem that currently the students choose online test questions blindly, the establishment of the item recommendation system is necessary. According to the student's level, an estimating algorithm is used to sort items and recommend the question which is in the front of the sort to the student. According to the study of student over a certain period of time and all the answers to the questions of a certain passage that students have given, a multiple attribute decision model based on positive and negative ideal point is established. The model can adjust the level of the student and the difficulty of test question adaptively and ensure that the system will recommend the most suitable test questions.

[1]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[2]  Tetsuya Murai,et al.  Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[3]  Massimo Franceschet,et al.  PageRank , 2010, Commun. ACM.

[4]  Wu Xiao,et al.  Comparison Study of Internet Recommendation System , 2009 .

[5]  Jöran Beel,et al.  Google Scholar's Ranking Algorithm: The Impact of Articles' Age (An Empirical Study) , 2009, 2009 Sixth International Conference on Information Technology: New Generations.

[6]  Zan Huang,et al.  A Comparative Study of Recommendation Algorithms in E- Commerce Applications , 2005 .

[7]  Enoch Peserico,et al.  Score and rank convergence of HITS , 2009, SIGIR.

[8]  Yixin Chen,et al.  Ranking on Data Manifold with Sink Points , 2013, IEEE Transactions on Knowledge and Data Engineering.

[9]  Marc Najork,et al.  Comparing the effectiveness of hits and salsa , 2007, CIKM '07.

[10]  Xiao-Dong Li,et al.  Comparison Study of Internet Recommendation System: Comparison Study of Internet Recommendation System , 2009 .

[11]  Rahila Patel,et al.  An Improved Ranking Scheme for Selection of Parents in Multi-Objective Genetic Algorithm , 2011, 2011 International Conference on Communication Systems and Network Technologies.

[12]  Andreas Spanias,et al.  An Adaptive Low Rank Algorithm for Semispherical Antenna Arrays , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[13]  Fan Qiang-xian.  Research and Improved Algorithm of HITS Based on Web Structure Mining , 2010 .

[14]  Weijun Lu,et al.  Concentration estimation of regularized ranking algorithm , 2011, 2011 International Conference on Multimedia Technology.

[15]  Marco Bressan,et al.  Local computation of PageRank: the ranking side , 2011, CIKM '11.

[16]  Diyi Yang,et al.  Serendipitous Personalized Ranking for Top-N Recommendation , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[17]  Zhang Wei,et al.  Webs ranking model based on pagerank algorithm , 2010, The 2nd International Conference on Information Science and Engineering.